PyTorch torch.log() method gives a new tensor having the natural logarithm of the elements of input tensor.
Syntax:
torch.log(input, out=None)Arguments
- input: This is input tensor.
- out: The output tensor.
Return: It returns a Tensor.
Let’s see this concept with the help of few examples:
Example 1:
Example 1:
# Importing the PyTorch library import torch # A constant tensor of size n a = torch.FloatTensor([5, 6, 7, 4]) print(a) # Applying the log function and # storing the result in 'out' out = torch.log(a) print(out) |
Output:
5 6 7 4 [torch.FloatTensor of size 4] 1.6094 1.7918 1.9459 1.3863 [torch.FloatTensor of size 4]
Example 2:
# Importing the PyTorch library import torch # A constant tensor of size n a = torch.FloatTensor([1.45, 2.3, 10]) print(a) # Applying the log function and # storing the result in 'out' out = torch.log(a) print(out) |
Output:
1.4500 2.3000 10.0000 [torch.FloatTensor of size 3] 0.3716 0.8329 2.3026 [torch.FloatTensor of size 3]
<!–
–>
Python | Tensorflow log() method
Python | cmath.log() method
sympy.log() method in Python
Python | PyTorch sin() method
Python | PyTorch sinh() method
Python | PyTorch cosh() method
Python | PyTorch tanh() method
Python | PyTorch cos() method
Python | PyTorch tan() method
Python | PyTorch asin() method
Please Login to comment…